تعیین و پیش‌بینی رخساره‌های منفذی براساس تلفیقی از داده‌های تزریق جیوه، پتروفیزیکی و پتروگرافی با استفاده از ترکیب شبکه‌های عصبی خودسازمان‏ده و ماشین بردار پشتیبان

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده زمین‌شناسی، پردیس علوم، دانشگاه تهران، ایران

2 گروه زمین‌شناسی نفت، پژوهشکده علوم کاربردی جهاد دانشگاهی، واحد دانشگاه شهید بهشتی، تهران، ایران

چکیده

شبکة منفذی کنترل‌کنندة رفتار سیالات در سنگ مخزن است. در مخازن کربناته به‏دلیل عدم‏تبعیت خصوصیات جریان سیال از بافت رسوبی اولیه، ویژگی‌های شبکه منفذی باید مستقیما در فرآیند تعیین رخساره استفاده شوند تا بتوان شرایط واقعی مخزن را تحلیل کرد. در این مطالعه با استفاده از ترکیب مطالعات پتروگرافی، پتروفیزیکی و مهندسی مخزن، رخساره‌های منفذی و سنگی در سازندهای کنگان و دالان در میدان گازی پارس جنوبی مطالعه شده‏اند. بر این اساس پنج رخساره منفذی با استفاده از روش شبکه‌های عصبی خودسازمان‏ده خصوصیات پتروفیزیکی، زمین‌شناسی و مخزنی منحصربه‏فردی دارند. براساس خصوصیات این رخساره‌های منفذی یک روند مشخص کاهش کیفیت مخزنی از رخساره یک به سمت رخساره پنج مشاهده شد. در نهایت برای ارتباط دادن رخساره‌های معرفی‏شده و نمودارهای پتروفیزیکی از روش ماشین بردار پشتیبان استفاده شد که با استفاده از روش میانگین مربعات خطا از نمودارهای چاه‌نگاری، رخساره‌های معرفی‏شده را با دقت 78% پیش‌بینی کرد.
 

کلیدواژه‌ها


عنوان مقاله [English]

Determination and prediction of Pore-facies based on a Compilation of Mercury Injection, Petrophysical and Petrographic Data Using a Hybrid of Self-organizing Neural Network and Support Vector Machine

نویسندگان [English]

  • ebrahim sfidari 1 2
  • Abdolhossein Amini 1
  • Ali Dashti 1
1 School of Geology, College of Science, University of Tehran, Iran|Department of Petroleum Geology, Iranian Academic Center for Education, Culture & Research, (ACECR), Tehran, Iran
2 School of Geology, College of Science, University of Tehran, Iran|Department of Petroleum Geology, Iranian Academic Center for Education, Culture & Research, (ACECR), Tehran, Iran
چکیده [English]

Pore network characteristics control the fluid condition in reservoir rocks. In carbonate reservoirs, fluid flow status is independent of primary depositional texture, so network properties must be directly included in the process of facies determination to accomplish them and make them be applicable for analyzing the reservoir real conduct. A compilation of petrographic, petrophysical and reservoir engineering studies is carried out to characterize pore throats and lithofacies using Self-organizing Map Neural Network in Dalan and Kangan formations of South Pars Gas Field in this paper. Five pore-facies with unique petrophysical, geological and reservoir features are determined by the applied network. A sharp decreasing trend in reservoir quality recognized from pore-facies 1 toward 5 based on their extracted properties. Meanwhile, Support Vector Machine (SVM) which was used  for prediction of pore-facies identified in previous steps from wireline well logs. The accuracy of the model in prediction of pore-facies is 78% which indicates an acceptable result for the model in the South Pars Gas Field.
 

کلیدواژه‌ها [English]

  • Pore Facies
  • Self-organizing Map Neural Network
  • Support Vector Machine
  • South Pars Gas Field
  • Dalan and Kangan Formation

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